Contemplating the compliance confounder in clinical trials with James Dai

Mindy Miner

James Dai, PhD has a unique background in molecular biology and biostatistical sciences, which exemplifies the mission of VIDD to use integrative disciplines for combating infectious diseases. Dai joined SCHARP in 2007 after receiving his PhD in Biostatistics from the University of Washington. Dai went to college in China, earning an MD in Basic Medicine from the Beijing University Medical Center and proceeded to earn masters degrees in Molecular Pathology from the University of Pittsburgh and Biostatistics from the University of Minnesota.

A randomized clinical trial can be thought of as the “gold standard” for assessing efficacy of a vaccine, treatment or behavioral intervention. In the example of a blinded 2-arm trial, study subjects are randomly assigned to a control or experimental arm without knowing which they are in and, after a certain pre-specified time, the experimental treatment is analyzed for efficacy.

HIV researchers in VIDD conduct many types of randomized trials with the goal of preventing or treating HIV. Several reasons exist for the failure of a trial to show a protective effect against HIV infection: the drug confers no benefit to the patient, the subjects were never exposed to the pathogen, or the drug was not taken. The latter instance is referred to as adherence or compliance. Trial statisticians must distinguish between a “drug effect” and “adherence effect.” Dr. James Dai, a statistician in the Statistical Center for HIV/AIDS Research and Prevention (SCHARP) housed in VIDD, researches the impact of compliance in HIV prevention clinical trials.

“In these clinical trials, the subjects don’t always take the prescribed regimen,” said Dai. “Indeed, in the HIV prevention field, the adherence can vary hugely.”

Dai performs assessments after trials are completed to ascertain the efficacy of antiretroviral drugs and microbicides in the prevention of HIV transmission and acquisition. Many of these drugs require daily or per-sex act administration, and proper compliance with the drug regimen is crucial for evaluating the treatment’s effectiveness. Dai and colleagues have uncovered interesting findings regarding drug adherence in HIV prevention clinical trials.

The ideal situation in a clinical trial is 100 percent adherence, but this just isn’t realistic, according to Dai. “People always want to say they did what they were supposed to do. There is an incentive there. They can feel obligated to say what the clinicians want to hear and thus adherence is very hard to measure,” Dai explained.

It is very difficult to predict at trial onset who will comply. However, Dai’s post-trial analyses showed that compliers and non-compliers had very different profiles. For example, a trial participant that is a “risk taker” seems more likely to be a non-complier, and highly educated participants tend toward drug adherence.

The recent advent of drug assays that track the levels of a drug in a volunteer’s blood have helped provide more quantitative measurements of drug adherence, although they depend on the specific drug’s pharmacologic characteristics and trial protocol’s frequency of sampling. The lack of placebo markers or tracers adds additional complexity to datum assessment.

Dai and colleagues in SCHARP apply statistical methods to answer the question: how do you estimate the effect among the subset of people actually taking the drug? One of their algorithms employs the assumption that if a subject didn’t take the drug, there would be no benefit.

“That’s the concept,” Dai said. “However, in reality, there are always deviations from this.”The findings emerging from these post-trial assessments illustrate the importance of monitoring participant compliance so researchers can take steps to increase drug adherence in future trials.

“There are research methodologies to be developed to infer, for example, what the treatment effect is among compliers. It is an important direction for me and for the SCHARP clinical trial research agenda,” Dai explained. “In this particular example as well as many other research problems, I believe statisticians can guide the field by carefully contemplating hypotheses and evidence.”